Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply   From and To filters
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

2 results

  • 2 - Linear Static Models with Additive Effects

  • Cheng Hsiao, University of Southern California
  • Book:
    Analysis of Panel Data
    Published online:
    19 May 2022
    Print publication:
    07 July 2022, pp 15-62

  • Summary

    Variable intercept linear regression through decomposing the error terms into the components of individual varying but time invariant; individual invariant but time varying; and individual-time varying component are introduced. Pros and cons of treating individual-specific and/or time-specific effects as fixed or random are discussed. Tests for fixed effects or random effects and ANOVA tests for the presence of individual or time-specifics are introduced.

  • A new stochastic restricted biased estimator under heteroscedastic or correlated error

  • Mustafa Ismaeel Alheety
  • Journal:
    ESAIM: Probability and Statistics / Volume 15 / 2011
    Published online by Cambridge University Press:
    22 February 2011, pp. 30-40
    Print publication:
    2011

  • In this paper, under the linear regression model with heteroscedastic and/or correlated errors when the stochastic linear restrictions on the parameter vector are assumed to be held, a generalization of the ordinary mixed estimator (GOME), ordinary ridge regression estimator (GORR) and Generalized least squares estimator (GLSE) is proposed. The performance of this new estimator against GOME, GORR, GLS and the stochastic restricted Liu estimator (SRLE) [Yang and Xu, Statist. Papers 50 (2007) 639–647] are examined in terms of matrix mean square error criterion. A numerical example is considered to illustrate the theoretical results.